Quantitative Portfolio Management with QuantInsti

Recommended for portfolio managers and quants who wish to construct their portfolio quantitatively, generate returns and manage risks effectively. In this course, you will learn different portfolio management techniques such as Factor Investing, Risk Parity and Kelly Portfolio, and Modern Portfolio Theory.

LIVE TRADING

• Code and backtest multi-factor portfolio strategy.
• Calculate the expected returns of an asset.
• Allocate capital using Kelly criterion, modern portfolio theory, and risk parity.
• Explain the CAPM and the Fama-french framework.
• Define different factors such as momentum, value, size and quality.
• Evaluate portfolio performance using Sharpe ratio, maximum drawdown and monthly performance.
• Paper trade and analyze the strategies and apply in live markets without any installations or downloads

SKILLS COVERED

Portfolio Management

Multi-Factor Strategy
Kelly Criterion
Risk Parity
Fama-French Three-Factor Model
Modern Portfolio Theory

Underlying Math

Linear Regression, Maximum Drawdown
Annualised Volatility
Covariance, Beta
Skewness, Kurtosis
Treynor Ratio, Information Ratio

Computation Skills

Pandas, NumPy, Math
OLS
CVXPY
Data Importing
Data Visualisation

PREREQUISITES

It is expected that you have some trading experience and understand basic financial markets terminology like ‘going long and short’. If you want to be able to code strategies in Python, then experience to store, visualise and manage data using Pandas and DataFrame is required. These skills are covered in our course ‘Python for Trading’.

SYLLABUS

Introduction

Overview of portfolio management using quantitative techniques.
Introduction to the Course
Course Structure
Quantra Features and Guidance

Basics of Portfolio Construction

Understand mathematical terms, such as covariance, returns and standard deviation of a portfolio, that are required to construct a portfolio.
Mathematical Terms for Portfolio Construction
Calculate Covariance
Interpret the Covariance Value
Calculate Portfolio Returns
Calculate Portfolio Standard Deviation
How to Use Jupyter Notebook?
Basics of Portfolio Construction
Calculate Portfolio Returns in Python
Calculate Covariance in Python
Calculate Portfolio Std Deviation in Python
Frequently Asked Questions

Modern Portfolio Theory

Calculate optimal weights by maximising mean-variance of the portfolio. Maximize returns per unit risk of the portfolio choosing stocks with less covariance. Simulate random weights and plot the Efficient Frontier.
Construct Two-Stock Portfolio using MPT
Objective of MPT
Choose the Portfolio Based on Covariance
Equi-Weighted Portfolio
Efficient Frontier
Targeted Risk
Implement Modern Portfolio Theory in Python
Choose the Portfolio – MPT
Plot the Efficient Frontier
Calculate Optimal Weights
Construct Multiple Stocks Portfolio using MPT
Returns of Portfolio with Multiple Stocks
Portfolio Standard Deviation – Matrix Form
Covariance Matrix

Kelly Criterion

Apply the Kelly Criterion to optimise the capital allocation
What is Utility?
The concept of Utility
The Utility Curve
The Kelly Criterion
The Kelly Criterion: Derivation
The Final Portfolio Value
The Daily Portfolio Value
Create a Portfolio Based on Kelly Criterion
Create an Array of Weights
Calculate the Final Portfolio Value
Create the Kelly Criterion
Create a Kelly Portfolio

Live Trading on Blueshift

This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.
Section Overview
Live Trading Overview
Vectorised vs Event Driven
Process in Live Trading
Real-Time Data Source
Blueshift Code Structure
Important API Methods
Schedule Strategy Logic
Fetch Historical Data
Place Orders
Backtest and Live Trade on Blueshift
Additional Reading
Blueshift Data FAQs

Live Trading Template

Blueshift Live Trading Template
Paper/Live Trading Kelly Criterion Strategy
FAQs for Live Trading on Blueshift

Risk Parity

Allocate capital to the securities in the portfolio such that each security contributes equally to the overall risk of the portfolio.
Construct Two-Stock Portfolio using Risk Parity
Risk Parity Approach
Basis of Risk Parity
Calculate Percentage Capital Allocation
Risk Parity
Calculate Weights using Risk Parity Approach
Portfolio with Multiple Stocks
Risk Parity for Multiple Stocks
Data Handling
Extension to ‘n’ Stocks
Portfolio Metrics
Sharpe Ratio
Risk Parity Relationship
Risk Parity vs Traditional Portfolio
Risk Parity Failure
Test on Capital Allocation

Beta

Understand and interpret beta of an asset. Calculate beta of an asset using different methods.
What is Beta?
Risk Exposure
Market Beta
Interpretation of Beta
Movement of Asset with Positive Beta
Movement of Asset with Negative Beta
Beta of an Asset in Python
Calculate Daily Returns
Calculate Beta

Capital Asset Pricing Model (CAPM)

Understand the Capital Asset Pricing Model and its limitations. Calculate expected returns of an asset using the capital asset pricing model.
Introduction to CAPM
Factors Affecting Expected Return
Calculate Expected Return on Asset
What is Security Market Line?
SML Characteristic
Stocks lie on the SML
Stocks lie above SML
Calculate Jensen’s Alpha
Fama-French Three- Factor Model
Understand the Fama-French three-factor model. Calculate expected returns using the Fama-French Three-Factor Model.

Fama-French Three-Factor Model

Factors of the Fama-French Model
Size Factor Exposure
High Book to Market Ratio Stock
Calculation of SMB and HML Factor
SMB Calculation
HML Calculation
Expected Returns using Fama-French Model
Calculate Beta of Fama-French Factors

Fama-French Five-Factor Model

Understand the Fama-French Five-Factor Model and its factors.
Fama-French Five-Factor Model
Profitability Factor
Investment Factor
Test on Beta, CAPM, and Fama-French

Factor Investing

Understand factor investing and different types of factors. How different factors work and their application in trading.
Factor Investing
Macroeconomic Factors
Good Factors
Applications of Factor Investing
Choose Factor Strategy
Benefits of Factor Investing
Which Factor Works Best?

Multi Factor Model

Understand momentum and short-term reversal factors. Create multiple factors and then combine them to form a multi-factor portfolio.
Multi-Factor Model: Momentum Factor
Stock Selection in Factor Model
Selection Criterion
Benefits of Negative Correlation
Assumption of Momentum Factor
Timeframe of a Factor
Interpretation of Momentum Factor
Multi-Factor Model: Reversal Factor
Short-Term Reversal Factor
Determination of Existing Trend
Interpretation of Short-Term Reversal Factor
The Momentum Factor in Python
Create the Momentum Factor
Stocks to Buy/Sell using Momentum Factor
Paper/Live Trading Momentum Factor Strategy
The Short-Term Reversal Factor in Python
Create the Short-Term Reversal Factor
Stocks to Buy/Sell using Short-Term Factor
Combine the Factors
Paper/Live Trading Multi-Factor Strategy
Test on Factors and Multi-Factor Investing

Portfolio Performance Analysis

Learn to analyze the portfolio using multiple performance measures such as Sharpe ratio, maximum drawdowns, Sortino ratio and many more metrics. Python code is provided to calculate all these performance metrics with an example.
Portfolio Performance Analysis
Calculate Sharpe Ratio in Python
Calculate Sortino Ratio in Python
Calculate Skewness in Python
Annualised Volatility
Calculate the Sortino Ratio
Calculate the Information Ratio
Calculate the Maximum Drawdown
Test on Performance Analysis and Paper Trading.

Run Codes Locally on Your Machine

Learn to install the Python environment in your local machine.
Python Installation Overview
Flow Diagram
Install Anaconda on Windows
Install Anaconda on Mac
Know your Current Environment
Troubleshooting Anaconda Installation Problems
Creating a Python Environment
Changing Environments
Quantra Environment
Troubleshooting Tips For Setting Up Environment
How to Run Files in Downloadable Section?
Troubleshooting For Running Files in Downloadable Section

Capstone Project

In this section, you will undertake a capstone project on real-world data. This project will require you to apply and practice the concepts learnt throughout this course.
Capstone Project: Getting Started
Problem Statement
Frequently Asked Questions
Template Code Files
Model Solution: QPM Capstone Project
Capstone Solution Downloadable

Summary

This section includes a downloadable zipped folder with all the codes and notebooks for easy access.
Summary
Python Codes and Data

ABOUT AUTHOR

QuantInsti®
QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.